Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Computer Architectures

    A tantárgy neve magyarul / Name of the subject in Hungarian: Számítógép-architektúrák

    Last updated: 2022. augusztus 31.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics     		BSc
    Course ID Semester Assessment Credit Tantárgyfélév
    VIHIAA03 2 3/1/0/v 5  
    3. Course coordinator and department Dr. Horváth Gábor,
    4. Instructors Dr. Horváth Gábor, egyetemi tanár, HIT
    Belső Zoltán, tudományos segédmunkatárs, HIT
    5. Required knowledge Boolean algebra; understanding of simple processor design and operation; reading knowledge of assembly language programs
    6. Pre-requisites
    Kötelező:
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    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

    Ajánlott:
    -
    7. Objectives, learning outcomes and obtained knowledge The aim of the course is to teach the design, operation and features of modern computers. Knowing the main characteristics of the hardware enables the development of efficient software that makes better use of the resources of computers.
    A tantárgy tanulási eredményei
    -    Information processing models, control flow architectures, instruction sets
    -    Fundamentals of I/O peripherals, traffic control, interrupts, DMA, interconnects
    -    PCI, PCI Express and USB interfaces
    -    Design, operation, and performance analysis of mass storage devices
    -    Memory technologies, DRAM based memory systems, design, operation and performance analysis
    -    Virtual memory management, concepts, operation, basic data structures, performance implications
    -    Cache memory, organisation, management
    -    Locality-aware programming techniques
    -    Pipeline based instruction execution, optimization
    -    Basic algorithms for out-of-order instruction execution, register renaming
    -    Wide pipelines, superscalar processors
    -    Branch prediction algorithms, branch prediction-aware programming techniques
    -    Forms of parallel processing, Flynn taxonomy
    -    Data parallelism, vector processors, SIMD instruction sets
    -    Classification of multiprocessor systems, basic concepts
    -    Fundamental problems of distributed memory management, cache coherence and memory consistency
    8. Synopsis Introduction (2 lectures). Introduction to information processing models. Control flow architectures: Neumann architecture, Harvard architecture, modified Harvard architecture. Characteristics of instruction sets, CISC-RISC strategies.
    Input-output peripherals (3 lectures). Dedicated I/O instructions and memory mapped peripheral management. Flow control. Processing peripheral signals: polling, interrupt, interrupt in multiprocessor environment, interrupt moderation. Processor offloading: DMA, I/O processor. Interconnections: bus, point-to-point, serial, parallel, timing, arbitration. Single-bus, multi-bus, bridge-based systems. PCI, PCI Express and USB interfaces.
    Mass storage devices (1 lecture). Operation of hard disks: concept and parts of a sector, zoned bit recording. Main components of data transfer service times. Queuing and reordering of commands. How SSD devices work: concept and role of pages, blocks. Management and side effects of read/write operations. Causes and importance of aging. Functions of SSD controllers.
    Memory (4 lectures). Synchronous DRAM based memory systems: concept and operation of memory controller, module, rank, bank. DRAM commands and their timing, out-of-order execution of commands. Virtual memory management: address translation, TLB, page table implementations, single level and hierarchical page tables. Cache memory: role of locality principles, cache organization, cache organization and virtual memory management. Cache content management: cache pollution prevention, block prefetching, block replacement algorithms. Locality-aware programming techniques.
    Processor (6 lectures). Pipeline instruction processing. Concepts and handling of hazards. Implementation of a simple 5 stage pipeline. Exception handling, precise interrupt handling. Handling arithmetic operations with different delays. Dynamic scheduling (out-of-order instruction execution). The concept of precedence graph and data flow-based instruction scheduling. The role and implementation of the instruction queue, register renaming, and the reorder buffer. The Tomasulo algorithm. Wide pipelines: superscalar, VLIW and EPIC architectures. Branch prediction: predicting the outcome of the jump condition and the jump target address. Branch prediction-aware programming. Attacks against speculative execution.
    Parallel processing (4 lectures) Data parallelism: vector processors, SIMD instruction set extensions. Multiprocessor systems: notion of explicit parallelism, multi-threaded processors, classification of multiprocessor systems, interconnection networks. Shared memory management: cache coherence protocols, memory consistency problems and solutions.


    The material of the classroom practices:
    - Review of digital design basics through a simple hardware-software design project
    - Calculating the relative load of the CPU for peripheral processing with polling and interrupt based I/O peripheral management
    - HDD latency and throughput calculations, manual walkthrough of SSD write management algorithms
    - Memory management: DRAM command scheduling, command latency and throughput calculations, exercises related to virtual memory management with and without TLB
    - Cache memory: practicing cache organization, cache error rate calculation and code optimization for simple C programs
    - Pipeline scheduling: scheduling low-level programs with various instruction pipelines, determining optimal instruction order
    - Advanced pipeline techniques: dependency analysis, elimination of anti-dependencies by register renaming, following the operation of branch predictors using simple C programs
    9. Method of instruction In two-week cycles, 1 or 2 lectures per week and 1 exercise every two weeks.
    10. Assessment

    To obtain the signature, one must score at least 40% in the mid-term test and participate in at least 70% of the classroom practices.

    The course is completed by passing the written (computer-based) exam. The minimum scores for the satisfactory, average, good and excellent marks are 40%, 55%, 70% and 85% respectively. In the case of a successful mid-term test, the part of the score above 60% will be added to the exam score.

    11. Recaps The mid-term test can be re-taken once during the semester and once during the re-take week.
    12. Consultations On the basis of individual appointment
    13. References, textbooks and resources Lecture slides published after the lectures
    14. Required learning hours and assignment
    Kontakt óra56
    Félévközi készülés órákra28
    Felkészülés zárthelyire22
    Házi feladat elkészítése0
    Kijelölt írásos tananyag elsajátítása0
    Vizsgafelkészülés44
    Összesen150
    15. Syllabus prepared by Dr. Horváth Gábor, egyetemi tanár, HIT